stabilityai / japanese-stable-diffusion-xl

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Total runs: 103
24-hour runs: 0
7-day runs: 0
30-day runs: 26
Model's Last Updated: July 10 2024
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Introduction of japanese-stable-diffusion-xl

Model Details of japanese-stable-diffusion-xl

Japanese Stable Diffusion XL

image

Please note: for commercial usage of this model, please see https://stability.ai/license

商用利用に関する日本語での問い合わせは [email protected] までお願い致します。

Model Details

Japanese Stable Diffusion XL (JSDXL) is a Japanese-specific SDXL model that is capable of inputting prompts in Japanese and generating Japanese-style images.

Usage

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained(
    "stabilityai/japanese-stable-diffusion-xl", trust_remote_code=True
)
pipeline.to("cuda")

# if using torch < 2.0
# pipeline.enable_xformers_memory_efficient_attention()

prompt = "柴犬、カラフルアート"

image = pipeline(prompt=prompt).images[0]
Model Details
  • Developed by : Stability AI
  • Model type : Diffusion-based text-to-image generative model
  • Model Description : This model is a fine-tuned model based on SDXL 1.0 . In order to maximize the understanding of the Japanese language and Japanese culture/expressions while preserving the versatility of the pre-trained model, we performed a PEFT training using one Japanese-specific compatible text encoder. As a PEFT method, we applied Orthogonal Fine-tuning (OFT) for better results and training stability.
  • License : STABILITY AI COMMUNITY LICENSE
Uses
Direct Use

Commercial use: for commercial usage of this model, please see https://stability.ai/license

商用利用に関する日本語での問い合わせは [email protected] までお願い致します。

Research: possible research areas/tasks include:

  • Generation of artworks and use in design and other artistic processes.
  • Applications in educational or creative tools.
  • Research on generative models.
  • Safe deployment of models which have the potential to generate harmful content.
  • Probing and understanding the limitations and biases of generative models.

Excluded uses are described below.

Out-of-Scope Use

The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.

Limitations and Bias
Limitations
  • The model does not achieve perfect photorealism
  • The model cannot render legible text
  • The model struggles with more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
  • Faces and people in general may not be generated properly.
  • The autoencoding part of the model is lossy.
Bias

While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.

How to cite
@misc{JSDXL, 
    url    = {[https://huggingface.co/stabilityai/japanese-stable-diffusion-xl](https://huggingface.co/stabilityai/japanese-stable-diffusion-xl)}, 
    title  = {Japanese Stable Diffusion XL}, 
    author = {Shing, Makoto and Akiba, Takuya and Chi, Jerry}
}
Contact

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103
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0
24-hour runs
0
3-day runs
0
7-day runs
26
30-day runs

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